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Biosens. Bioelectron., Available online 19 March 2025, 117399 | https://doi.org/10.1016/j.bios.2025.117399
Artificial Intelligence in Bacterial Diagnostics and Antimicrobial Susceptibility Testing: Current Advances and Future Prospects
Seungmin Lee 1,2,†, Jeong Soo Park 1,3,†, Ji Hye Hong 1,2, Hyowon Woo 1, Changhyun Lee 4, Ju Hwan Yoon 1,4, Ki-Baek Lee 4, Seok Chung 3,#, Dae Sung Yoon 2,5,6,#, Jeong Hoon Lee 1,7,#
1KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
2School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Republic of Korea
3School of Mechanical Engineering, Korea University, 145 Anam-ro, Seoungbuk-gu, Seoul 02841, Republic of Korea
4Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul 01897, Republic of Korea
5Interdisciplinary Program in Precision Public Health, Korea University, Seoul 02841, Republic of Korea
6Astrion Inc, Seoul 02841, Republic of Korea
7Department of Integrative Energy Engineering, College of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
†These authors contributed equally.
#These authors jointly supervised this work: Seok Chung, Dae Sung Yoon, Jeong Hoon Lee
1KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
2School of Biomedical Engineering, Korea University, 145 Anam-ro, Seongbuk, Seoul 02841, Republic of Korea
3School of Mechanical Engineering, Korea University, 145 Anam-ro, Seoungbuk-gu, Seoul 02841, Republic of Korea
4Department of Electrical Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon, Seoul 01897, Republic of Korea
5Interdisciplinary Program in Precision Public Health, Korea University, Seoul 02841, Republic of Korea
6Astrion Inc, Seoul 02841, Republic of Korea
7Department of Integrative Energy Engineering, College of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
†These authors contributed equally.
#These authors jointly supervised this work: Seok Chung, Dae Sung Yoon, Jeong Hoon Lee
Abstract
Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and AST by leveraging machine learning models, including Random Forest, Support Vector Machines (SVM), and deep learning architectures such as Convolutional Neural Networks (CNNs) and transformers. The integration of AI into these methods promises to address the current limitations of traditional techniques, offering a path toward more efficient, accessible, and reliable diagnostic solutions. In particular, AI-based approaches have demonstrated significant potential in resource-limited settings by enabling cost-effective and portable diagnostic solutions, reducing dependency on specialized infrastructure, and facilitating remote bacterial detection through smartphone-integrated platforms and telemedicine applications. This review highlights AI’s transformative role in automating data analysis, minimizing human error, and delivering real-time diagnostic results, ultimately improving patient outcomes and optimizing healthcare efficiency. In addition, we not only examine the current advances in machine learning and deep learning but also review their applications in plate counting, mass spectrometry, morphology-based and motion-based microscopic detection, holographic microscopy, colorimetric and fluorescence detection, electrochemical sensors, Raman and Surface-Enhanced Raman Spectroscopy (SERS), and Atomic Force Microscopy (AFM) for bacterial diagnostics and AST. Finally, we discuss the future directions and potential advancements in AI-driven bacterial diagnostics.
논문정보
- Research article
- 2025년 03월 (BRIC 등록일 2025-03-30)
- 국내 연구진
- 바이오·의료융합 > 바이오센싱 및 나노바이오물질
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